Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. You can create months worth of debt in a matter of one working day and even the most experienced teams can miss a moment when the debt is so huge that it sets them back for half a year, which is often enough to kill a fast-pacing project. You end up with the project where the metrics randomly jump up or down, do not reflect the actual quality, and you are not able to improve them.
As smaller organizations move to public cloud, the remaining private datacenters are also getting much larger. A big driver for this scale is data leading to a completely new set of storage architectures that can operate a large scale and require very little management of the data. A new class of storage vendor has emerged, whose solutions accomplish this goal through a combination of 1) software defined storage 2) commodity building block hardware componentry 3) distributed scalable storage architectures and 4) application awareness. Let’s look at each of these solution characteristics and how they make large scale datacenter operations cost effective.
As a product manager, you need to capture, understand, interpret and analyze a range of qualitative and quantitative inputs to arrive at the ideal product decisions. There is no one single source of "truth" exists when making product decisions. Some inputs are definitely more quantitative than others, but many also need a dialogue with customers as well as judgment calls, best guesses, and interpretations. There is no standard approach or single tool that meets everyone's needs. So what's the answer? Is product management more art than science?
To stay competitive and successful, both FinTech software developers and financial companies need to catch the waves of digital disruption and learn how to ride them right. To keep up with the finnovation pace, businesses are adopting the emerging technologies such as Data Science, AI, digital currency, Blockchain, Biometrics, and more. However, they may turn out to be intricate and present challenges you need to be ready to embrace. Here are 5 innovations FinTech software developers need to be ready to adopt to implement FinTech innovations with sense and caution.
In the ‘outcome economy,’ people don’t buy things. They buy outcomes. They buy the end results they are looking for. When a manufacturer sells a product-as-a-service, it is a sizeable step towards an outcome-based business model. So, what about enterprise software. Why do most enterprises still keep paying for the ‘thing’ (i.e. the software) rather than the outcome that they desire? Or to flip the question around, are there any enterprise solution providers offering outcome-based payment models? Do any enterprise software companies actually sell results rather than software?
The focus of AI implementation at present must be to minimize human involvement in the routine and non-creative tasks, so that human effort can be directed towards innovation and planning, where AI can be used for guidance. Due to its deep learning and independent decision-making capabilities, applications of AI in different business areas are seeing a steady rise in ubiquity in some industries. The concept of artificial intelligence or machines that aim to emulate human thinking is undergoing vigorous research. Here are a few application areas that you can consider for AI implementation.
Each year, Earth Day provides an opportunity for people around the world to consider how they can take action to protect our environment. For decades, it has encouraged people to undertake individual actions and advocate for policies. Digital technologies – in particular the IoT – can help us address the climate change challenge by accelerating the transition to a more sustainable, renewable-energy-powered economy. In particular, the IoT is a key enabling technology for an emerging concept called, “The Internet of Energy.”
Data is clearly not what it used to be! Organizations of all types are finding new uses for data as part of their digital transformations. New data is transactional and unstructured, publicly available and privately collected, and its value is derived from the ability to aggregate and analyze it. We can divide this new data into two categories: big data and fast data. The big data–fast data paradigm is driving a completely new architecture for data centers.I will cover each of the top five data challenges presented by new data center architectures
Most IoT ecosystem projects will involve multiple contributing application partners. They will also involve complex, evolving functional and non-functional requirements. To address these challenges and reduce complexity, IoT developers are now starting to embrace collaborative lifecycle management (CLM) technologies combined with the latest continuous engineering (CE) technologies. Organizations that want to ensure the success of complex IoT initiatives will need to mitigate the adverse potential of complexity-related development risk. Thankfully, these risks can be managed effectively using the latest CE/CLM tools to ensure that your IoT vision becomes a reality. Collaborative lifecycle management is a key to IoT success
We can see a lot of hype about AI and Machine Learning, and its potential to transform businesses. More and more companies are adopting machine learning solutions, setting up accelerators, opening R&D centers, and investing into startups. Also, there is a large number of reports with AI market estimates and forecasts. However, it’s challenging to get the right information on machine learning development that will actually work for your business. Here are our five expert tips to make machine learning development work for you.
With growing amounts of computational power, machine learning and deep learning are increasingly making their way into numerous sectors. They are widely used for recognizing objects, translating speech in real-time, determining potential outcomes, understanding consumer habits, making personalized recommendations, and a lot more. Still, some questions remain uncertain. What do these two entail? And should companies invest in machine learning development to benefit their businesses? We provide the answers to these questions in our article.
GAN (Generative Adversarial Network) addresses the lack of imagination haunting deep neural networks, the popular AI structure that roughly mimics how the human brain works. GAN technique proposes that you use two neural networks to create and refine new data. There are many practical applications for GAN. GANs might prove to be an important step toward inventing a form of general AI, artificial intelligence that can mimic human behavior and make decisions and perform functions without having a lot of data. GANs can’t invent totally new things. You can only expect them to combine what they already know in new ways.
What do artificial intelligence (AI), invention, and social good have in common? While on the surface they serve very different purposes, at their core, they all require you to do one thing in order to be successful at them: think differently. When it comes to AI, invention, and social good, the possibilities are endless. Technology will only continue to become more advanced, creating new opportunities to fix societal problems related to health, sustainability, conservation, accessibility, and much more. If you’re thinking of jumping into AI for good, just remember the most important rule: think differently.
IoT can be used not only to improve existing business operations but also to create new offerings and new business models. Business models require thinking through the consumption side of your offer — how it is bought and used, and the production side of the offer — how it is created and delivered. We need to think of the offer from the consumer’s lens, i.e. buyer, user, and operator. It is time to go beyond predictive maintenance and reimagining our offerings with IoT. Time to flip some tiles!
As companies scale transaction volumes and integrate with more and more third party software, they get a growing inflow of data and services. On the downside, this subsequently increases the risk of data breaches and cyber-attacks. fintechs currently suffer from the mismatch between innovations and regulations as the latter don’t keep up with the technological advancement in the financial industry. What’s more, early-stage startups usually don’t have adequate compliance teams. Such unstable regulatory environment creates additional security and compliance challenges for the financial market players. Then how to scale up without compromising security?
Artificial Intelligence has been buzzing around more frequently with higher and ever-increasing intensity every passing day. Reason is as clear as a crystal: Its power and possibilities it can create. It doesn’t really matter who thinks of AI highly or lowly but one thing is certain: Its time has come. Before you buy time in realizing and delay in deciding of its adoption, it may come and stare straight in your face! You may not even have the bargaining power that you still enjoy today.
VPN evolution has transitioned the technology from point-to-point connectors that facilitate remote access to one that's based on sophisticated security multipoint connectivity. VPN evolution has taken place over the years, adapting to the networks that have been shaped by broadband connectivity, the cloud, and mobility, as well as the endpoint devices themselves. Reflecting back on the early days of VPNs and how far we have come, the evolution and the history of VPN technology can be broken down into four phases. Secure communication is one of the most important foundations for our future, and it is imperative to protect data in motion with VPN evolution.
Data has become important for everyone like never before, because it makes us to take informed decisions, improve operations. We can only improve things & activities which we can measure, and when we measure anything, it is described in a form of data. If you want to leverage and operationalize data proactively, you need to invest in your underlying data architecture and compile the information map for your organization. Solid information architecture will also set up your foundation for a data governance program. You have to know what the data is and assign business meaning to it, with the proper terminology.
we continue to explore how technologies can help fintechs solve scalability challenges. We’ll try to answer the following question: How fintechs can find new revenue streams and extend their market reach? When fintechs find the technological capacity to build a scalable and reliable solution and manage to keep their operational costs low, they want to grow bigger, raise profits, and scale their business reach. However, that may be a daunting task due to strong cards of other financial services companies operating in the market.
Helping to fuel interest in data lakes are the digital transformation efforts underway at many enterprises, spurred by the emergence of the Internet of Things (IoT). The connected objects in the IoT will generate huge volumes of data. As more products, assets, vehicles and other “things” are instrumented and data ingested, it’s important that IoT data sets be aggregated in a single place, where they can be easily analyzed and correlated with other relevant data sets using big data processing capabilities. Doing so is critical to generating the most leverage and insight from IoT data.
Bitcoin is the leading edge of a movement I think of as Open Source money, and here are a few ways of thinking about this that might help. Here are 100 things to know for those new to bitcoin. The goal isn’t for you to understand all of these assertions, but if they contain unfamiliar words or concepts, you can then google those concepts and become informed. Understanding what is behind these 100 assertions will help you become knowledgeable about Bitcoin. If you want you can see how many of these things you already knew and give yourself a score out of 100 at the end.
What problems do fintechs need to solve to scale up and grow profits? They need building an easily scalable software product, partnering with other companies and engaging new customer segments, and complying with regulation and security standards while scaling up. In our series of articles we will dwell on how technologies can help you solve these 3 key challenges. We've collected and analysed findings from PWC, CBInsights, Forbes, etc., and fintech software development cases to elaborate a strategy on how to build a successful fintech business that generates profits, attracts investments and can achieve economies of scale.
Today we understand our customers better than ever. The data we gather and analyze determines the success of our business. Business Intelligence, big data, data science, and data analytics provide companies with illuminating insights. Those who adopt these technologies early, gain huge competitive advantages. Therefore, the demand for professional data analysts is far exceeding the supply. To deal with the scarcity of specialists and soaring prices for their services, many business leaders consider data analysis outsourcing. But how to make it right? Our article will highlight the most important aspects of data analysis outsourcing and tips on choosing the best data analysts.
Many startups invest too heavily in sales before really understanding their customers. Whether it is based on the belief that good salespeople can sell anything, spending too much money on sales personnel too early is a good way to fail fast in a bad way. Bringing on your sales talent before you are ready, can costs start-ups millions of dollars. Hiring your sales talent should instead be a natural outcome of understanding your customers, developing your segment playbooks and your learnings (wins and losses) from customer interactions.